Meta plasticity and Continual Learning: Mechanisms subserving Brain Computer Interface Proficiency
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Objective
Brain Computer Interfaces (BCIs) require substantial cognitive flexibility to optimize control performance across diverse settings. Remarkably, learning this control is rapid, suggesting it might be mediated by neuroplasticity mechanisms operating on very short time scales. However, these mechanisms remain far from understood. Here, we propose a meta plasticity model of BCI learning and skill consolidation at the single cell and population levels comprised of three elements: a) behavioral time scale synaptic plasticity (BTSP), b) intrinsic plasticity (IP) and c) synaptic scaling (SS) operating at time scales from seconds to minutes to hours and days. Notably, the model is able to explain representational drift – a frequent and widespread phenomenon observed in multiple brain areas that adversely affects BCI control and continued use.
Approach
We developed a closed loop, all optical approach to characterize IP, BTSP and SS with single cell resolution in cortical L2/3 of awake mice using fluorescent two photon (2P) GCaMP7s imaging and optogenetic stimulation of the soma targeted ChRmine Kv2.1 . We further trained mice on a one-dimensional (1D) BCI control task and systematically characterized within session (seconds to minutes) learning as well as across sessions (days and weeks) with different neural ensembles.
Main results
We found that on the time scale of seconds, substantial BTSP could be induced and was associated with significant IP over minutes. Over the time scale of days and weeks, these changes could predict BCI control proficiency, suggesting that BTSP and IP might be complemented by SS to stabilize and consolidate BCI control.
Significance
Our results provide theoretical and early experimental support for an integrated meta plasticity model of continual BCI learning and skill consolidation. The model predictions may be used to design and calibrate neural decoders with complete autonomy while considering the temporal and spatial scales of plasticity mechanisms and their anticipated order of occurrence. With the power of modern-day machine learning (ML) and artificial Intelligence (AI), fully autonomous neural decoding and adaptation in BCIs might be achieved with minimal to no human intervention.
